@article{fischell2017,
title = {Supervised Machine Learning for Estimation of Target Aspect Angle From
Bistatic Acoustic Scattering},
author = {Erin M. Fischell and Henrik Schmidt},
journal = {IEEE Journal of Oceanic Engineering},
pages = {1-11},
number = {99},
volume = {PP},
year = {2017},
keywords = {Acoustics;Antenna radiation patterns;Data models;Global Positioning
System;Marine vehicles;Scattering;Steel;Machine learning;swimming
robots;underwater acoustics},
abstract = {When an aspect-dependent target is insonified by an acoustic source, distinct
features are produced in the resulting bistatic scattered field. These
features change as the aspect between the source and the target is varied.
This paper describes the use of these features for estimation of the target
aspect angle using data collected by an autonomous underwater vehicle (AUV).
An experiment was conducted in November 2014 in Massachusetts Bay to collect
data using a ship-based acoustic source producing 7–9-kHz linear frequency
modulation (LFM) chirps insonifying a steel pipe. The true target orientation
was unknown, as the target was dropped from the ship with no rotation control.
The AUV Unicorn, fitted with a 16-element nose array, was deployed in data
collection behaviors around the target, and the ship was moved to create two
target aspects. A support vector machine regression model was trained using
simulated scattering bistatic field data. This model was then used to estimate
the target aspect angle from the data collected during the experiment. The
difference between the estimates was consistent with experimental observations
of relative source positioning. The simulation-based model appeared successful
in estimating the target aspect angle despite uncertainties in target and
source location and mismatch between true environment and simulation
parameters.}}